Kim, J orcid.org/0000-0002-3456-6614 and Kadem, O (2023) Real-Time State of Charge-Open Circuit Voltage Curve Construction for Battery State of Charge Estimation. IEEE Transactions on Vehicular Technology, 72 (7). 8613 -8622. ISSN 0018-9545
Abstract
All state of charge (SoC) estimation algorithms based on equivalent circuit models (ECMs) estimate the open circuit voltage (OCV) and convert it to the SoC using the SoC-OCV nonlinear relation. These algorithms require the identification of ECM parameters and the nonlinear SoC-OCV relation. In literature, various techniques are proposed to simultaneously identify the ECM parameters. However, the simultaneous identification of the SoC-OCV relation remains challenging. This paper presents a novel technique to construct the SoC-OCV relation, which is eventually converted to a single parameter estimation problem. The Kalman filter is implemented to estimate the SoC and the related states in batteries using the proposed parameter estimation and the SoC-OCV construction technique. In the numerical simulations, the algorithm demonstrates that it accurately estimates the battery model parameters, and the SoC estimation error remains below 2%. We also validate the proposed algorithm with a battery experiment. The experimental results show that the error in SoC estimation remains within 2.5%.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Keywords: | Batteries , Estimation , Mathematical models , Integrated circuit modeling , Adaptation models , Battery charge measurement , Parameter estimation |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mechanical Engineering (Leeds) > Institute of Engineering Systems and Design (iESD) (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 16 Feb 2023 08:44 |
Last Modified: | 22 Aug 2024 13:43 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/TVT.2023.3244623 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:196412 |